Deep Learning with LiDAR Point Cloud Data for Automatic Roadway Health Monitoring

Traditional methods for monitoring road conditions are fraught with challenges. Field inspections are labor-intensive and costly, aerial photography is subjective, and mobile measurement systems (MMS) require substantial investment in geospatial technology. In response to these limitations, there is a growing interest in leveraging advanced 3D scanning technologies, such as LiDAR and RGB-D scanners, in conjunction with deep learning algorithms for infrastructure assessment. 3D point cloud data, analyzed through deep learning models, offers several advantages over traditional 2D-based computer vision techniques. These include enhanced spatial resolution, superior object recognition, and the ability to handle complex scenes more effectively. However, this approach also introduces challenges, such as greater computational demands and the need for specialized hardware. Therefore, albeit with the tremendous benefits associated with the 3D point cloud, there are very few studies dedicated to the application of 3D point cloud-based deep learning models to the infrastructure operation and assessment. To bridge this research gap, this study aims to investigate the efficacy of various point cloud-based deep learning models in automating roadway health assessments. Given the vital nature of this topic, the research will evaluate promising deep learning architectures, such as PointNet, PointNet++, 3D-CNNs, and PointCNN, using point cloud data gathered from multiple roadways in Southern California. Additionally, some typical technical challenges such as the noise filtering, data alignment, and dimension reduction via resampling, etc., will be further explored. This investigation aims to offer valuable insights into the pros and cons of these models under diverse conditions, thereby contributing to future research in this emerging area. Most importantly, these applications combine to offer a more comprehensive, real-time understanding of roadway health, facilitating proactive maintenance, reducing costs, and improving public safety.

• Record URL:
  https://tbd.ctr.utexas.edu/research-product/deep-learning-with-lidar-point-cloud-data-for-automatic-roadway-health-monitoring/

Language

• English

Project

• Status: Active
• Funding: $250000
• Contract Numbers:

  69A3552344815

  69A3552348320

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Understanding Future of Travel Behavior and Demand

  University of Texas
  Austin, TX  United States 
• Project Managers:

  Bhat, Chandra

• Performing Organizations:

  California State Polytechnic University, Pomona

  3801 West Temple Avenue
  Pomona, CA  United States  91768
• Principal Investigators:

  Zhang, Yongping

• Start Date: 20231101
• Expected Completion Date: 20250531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Data collection; Highways; Laser radar; Machine learning; Point clouds; Structural health monitoring
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01917638
• Record Type: Research project
• Source Agency: Data-Supported Transportation Operations and Planning Center
• Contract Numbers: 69A3552344815, 69A3552348320
• Files: UTC, RIP
• Created Date: May 6 2024 3:35PM